Cardiotocogram Dataset

Ignacio Vellido Expósito

https://archive.ics.uci.edu/ml/datasets/cardiotocography

Data Set Information:
    2126 fetal cardiotocograms (CTGs) were automatically processed and the respective 
    diagnostic features measured. The CTGs were also classified by three expert 
    obstetricians and a consensus classification label assigned to each of them. 
    Classification was both with respect to a morphologic pattern (A, B, C. ...) 
    and to a fetal state (N, S, P). Therefore the dataset can be used either for 
    10-class or 3-class experiments.

Exam data       
    FileName    of CTG examination      
    Date    of the examination      
    b   start instant       
    e   end instant     
Measurements        
    LBE baseline value (medical expert)     
    LB  baseline value (SisPorto)       
    AC  accelerations (SisPorto)        
    FM  foetal movement (SisPorto)      
    UC  uterine contractions (SisPorto)     
    ASTV    percentage of time with abnormal short term variability  (SisPorto)     
    mSTV    mean value of short term variability  (SisPorto)        
    ALTV    percentage of time with abnormal long term variability  (SisPorto)      
    mLTV    mean value of long term variability  (SisPorto)     
    DL  light decelerations     
    DS  severe decelerations        
    DP  prolongued decelerations        
    DR  repetitive decelerations        
    Width   histogram width     
    Min low freq. of the histogram      
    Max high freq. of the histogram     
    Nmax    number of histogram peaks       
    Nzeros  number of histogram zeros       
    Mode    histogram mode      
    Mean    histogram mean      
    Median  histogram median        
    Variance    histogram variance      
    Tendency    histogram tendency: -1=left assymetric; 0=symmetric; 1=right assymetric     
Classification      
    A   calm sleep      
    B   REM sleep       
    C   calm vigilance      
    D   active vigilance        
    SH  shift pattern (A or Susp with shifts)       
    AD  accelerative/decelerative pattern (stress situation)        
    DE  decelerative pattern (vagal stimulation)        
    LD  largely decelerative pattern        
    FS  flat-sinusoidal pattern (pathological state)        
    SUSP    suspect pattern     
    CLASS   Class code (1 to 10) for classes A to SUSP      
    NSP Normal=1; Suspect=2; Pathologic=3

Vamos a clasificar respecto a la última variable (NSP), el resto de columnas de clasificación las quitaremos.

También eliminaremos la columnas con información del exámen médico por no ser medidas usadas para etiquetar los datos.

Nos quedará por tanto un problema de clasificación multivariable con tres posibles etiquetas.